Statistical Methods for the Analysis of ChlP-chip Data

描述(由申请人提供)：随着许多基因组测序项目接近尾声，最大的剩余挑战是理解这些序列中编码的信息。识别转录因子(TF)及其DMA结合位点之间的相互作用是这一挑战的组成部分。这些相互作用控制着细胞功能的关键步骤，它们的功能障碍可以显著促进各种疾病的进展。ChlP芯片实验将染色质免疫沉淀与DMA微阵列分析相结合，已成为全基因组鉴定和表征转录因子结合位点的有力工具。这些实验产生了大量重复次数较少的噪声数据，因此需要创新的稳健统计分析方法。这项建议的目标是开发、评估和传播用于分析Chlp芯片实验数据的统计方法。这些目标将通过四个具体目标来实现：(1)发展稳健的概率方法来检测Tf结合区。这些方法将利用在平铺阵列上的探针之间通用的信息，以在小样本大小中增加功率。(2)对AIM-1中的方法进行了扩展，以处理探测器序列重叠且来自附近探测器的观测显示出远程空间相关性的阵列设计。因此，我们将为一般的平铺阵列设计开发严格的统计推断程序。(3)开发了一个自适应框架，用于将ChlP-ChIP实验的定量信息纳入基序发现。这将把ChlP-ChIP数据分析的第一阶段，即结合区域的识别与下游序列分析联系起来，从而提高基序寻找任务的敏感性和特异性。(4)在统计资料包中执行作为这项研究的一部分而制定的统计方法。产生的成套资料包将以独立版本和生物导体项目的一部分向科学界提供，该项目是一个用于分析基因组数据的开放源码和开发软件项目。这项拟议研究的成功完成将大大改进Chlp芯片实验分析的统计方法。

DESCRIPTION (provided by applicant): With many genome-sequencing projects coming to an end, the biggest remaining challenge is to comprehend the information encoded in these sequences. Identifying interactions between transcription factors (TFs) and their DMA binding sites is an integral part of this challenge. These interactions control critical steps in cell functions, and their dysfunction can significantly contribute to the progression of various diseases. ChlP-chip experiments that couple chromatin immunoprecipitation with DMA microarray analysis have become powerful tools for the genome-wide identification and characterization of transcription factor binding sites. These experiments produce massive amounts of noisy data with small number of replicates and therefore require innovative robust statistical analysis methods. The objectives of this proposal are to develop, evaluate and disseminate statistical methods for analyzing data from ChlP-chip experiments. These objectives will be accomplished through four specific aims: (1) Development of robust probabilistic methods for detecting TF bound regions. These methods will utilize the information common across probes on tiling arrays to increase power in small sample sizes. (2) Extension of the methods in Aim-1 to deal with array designs where probe sequences overlap and observations from nearby probes exhibit long-range spatial dependencies. As a result, we will develop rigorous statistical inference procedures for general tiling array designs. (3) Development of an adaptive framework for incorporating quantitative information from ChlP-chip experiments into motif finding. This will connect the first stage of the ChlP-chip data analysis, namely identification of the bound regions, with the downstream sequence analysis thereby boosting the sensitivity and specificity of the motif finding task. (4) Implementation of the statistical methods developed as part of this research in statistical packages. The resulting packages will be available to the scientific community both in stand-alone versions and as part of the Bioconductor Project which is an open source and development software project for the analysis of the genomic data. Successful completion of the proposed research will result in substantially improved statistical methods for the analysis of ChlP-chip experiments.